## Using HunyuanDiT ControlNet ### Instructions The dependencies and installation are basically the same as the [**base model**](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1). We provide three types of ControlNet weights for you to test: canny, depth and pose ControlNet. Download the model using the following commands: ```bash cd HunyuanDiT # Use the huggingface-cli tool to download the model. huggingface-cli download Tencent-Hunyuan/HYDiT-ControlNet --local-dir ./ckpts/t2i/controlnet # Quick start python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type canny --prompt "在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围" --condition_image_path controlnet/asset/input/canny.jpg --control_weight 1.0 ``` Examples of condition input and ControlNet results are as follows:
Condition Input
Canny ControlNet Depth ControlNet Pose ControlNet
在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围
（At night, an ancient Chinese-style lion statue stands in front of the hotel, its eyes gleaming as if guarding the building. The background is the hotel entrance at night, with a close-up, eye-level, and centered composition. This photo presents a realistic photographic style, embodies Chinese sculpture culture, and reveals a mysterious atmosphere.） 		在茂密的森林中，一只黑白相间的熊猫静静地坐在绿树红花中，周围是山川和海洋。背景是白天的森林，光线充足
（In the dense forest, a black and white panda sits quietly in green trees and red flowers, surrounded by mountains, rivers, and the ocean. The background is the forest in a bright environment.） 		一位亚洲女性，身穿绿色上衣，戴着紫色头巾和紫色围巾，站在黑板前。背景是黑板。照片采用近景、平视和居中构图的方式呈现真实摄影风格
（An Asian woman, dressed in a green top, wearing a purple headscarf and a purple scarf, stands in front of a blackboard. The background is the blackboard. The photo is presented in a close-up, eye-level, and centered composition, adopting a realistic photographic style）
Image 0 Image 1 Image 2
ControlNet Output
Image 3 Image 4 Image 5
### Training We utilize [**DWPose**](https://github.com/IDEA-Research/DWPose) for pose extraction. Please follow their guidelines to download the checkpoints and save them to `hydit/annotator/ckpts` directory. Additionally, ensure that you install the related dependencies. ```bash pip install matplotlib pip install onnxruntime_gpu ``` We provide three types of weights for ControlNet training, `ema`, `module` and `distill`, and you can choose according to the actual effects. By default, we use `distill` weights. Here is an example, we load the `distill` weights into the main model and conduct ControlNet training. If you want to load the `module` weights into the main model, just remove the `--ema-to-module` parameter. If apply multiple resolution training, you need to add the `--multireso` and `--reso-step 64` parameter. ```bash task_flag="canny_controlnet" # task flag is used to identify folders. control_type=canny resume=./ckpts/t2i/model/ # checkpoint root for resume index_file=path/to/your/index_file results_dir=./log_EXP # save root for results batch_size=1 # training batch size image_size=1024 # training image resolution grad_accu_steps=2 # gradient accumulation warmup_num_steps=0 # warm-up steps lr=0.0001 # learning rate ckpt_every=10000 # create a ckpt every a few steps. ckpt_latest_every=5000 # create a ckpt named `latest.pt` every a few steps. sh $(dirname "$0")/run_g_controlnet.sh \ --task-flag ${task_flag} \ --control_type ${control_type} \ --noise-schedule scaled_linear --beta-start 0.00085 --beta-end 0.03 \ --predict-type v_prediction \ --multireso \ --reso-step 64 \ --ema-to-module \ --uncond-p 0.44 \ --uncond-p-t5 0.44 \ --index-file ${index_file} \ --random-flip \ --lr ${lr} \ --batch-size ${batch_size} \ --image-size ${image_size} \ --global-seed 999 \ --grad-accu-steps ${grad_accu_steps} \ --warmup-num-steps ${warmup_num_steps} \ --use-flash-attn \ --use-fp16 \ --use-ema \ --ema-dtype fp32 \ --results-dir ${results_dir} \ --resume-split \ --resume ${resume} \ --ckpt-every ${ckpt_every} \ --ckpt-latest-every ${ckpt_latest_every} \ --log-every 10 \ --deepspeed \ --deepspeed-optimizer \ --use-zero-stage 2 \ "$@" ``` Recommended parameter settings | Parameter | Description | Recommended Parameter Value | Note| |:---------------:|:---------:|:---------------------------------------------------:|:--:| | `--batch_size` | Training batch size | 1 | Depends on GPU memory| | `--grad-accu-steps` | Size of gradient accumulation | 2 | - | | `--lr` | Learning rate | 0.0001 | - | | `--control_type` | ControlNet condition type, support 3 types now (canny, depth and pose) | / | - | ### Inference You can use the following command line for inference. a. Using canny ControlNet during inference ```bash python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type canny --prompt "在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围" --condition_image_path controlnet/asset/input/canny.jpg --control_weight 1.0 ``` b. Using pose ControlNet during inference ```bash python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type depth --prompt "在茂密的森林中，一只黑白相间的熊猫静静地坐在绿树红花中，周围是山川和海洋。背景是白天的森林，光线充足" --condition_image_path controlnet/asset/input/depth.jpg --control_weight 1.0 ``` c. Using depth ControlNet during inference ```bash python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type pose --prompt "一位亚洲女性，身穿绿色上衣，戴着紫色头巾和紫色围巾，站在黑板前。背景是黑板。照片采用近景、平视和居中构图的方式呈现真实摄影风格" --condition_image_path controlnet/asset/input/pose.jpg --control_weight 1.0 ```